Indices, deposits, mines, and quarries include information relating to architectural crushed or industrial stone, non-metallic substances, and metallic substances.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

A predictive model for Canadian carbonatite-hosted REE ± Nb deposits is presented herein. This model was developed by integrating diverse data layers derived from geophysical, geochronological, and geological sources. These layers represent the key components of carbonatite-hosted REE ± Nb mineral systems, including the source, transport mechanisms, geological traps, and preservation processes. Deep learning algorithms were employed to integrate these layers into a comprehensive predictive framework. Here is a link to the publication that describes this product: https://link.springer.com/article/10.1007/s11053-024-10369-7

The Mining index maps application is an interactive map application that allows users to select static quartz and/or placer claim maps.

These products represent crop health indices derived from the Versatile Soil Moisture Budget (VSMB) model using crop specific coefficients and station based precipitation and temperature measurements to simulate crop growth. The VSMB model simulates soil moisture dynamics and water stress conditions based on water availability in the soil profile and simulated evapotranspiration during the crop growing season. Crop phenological stages, which are related to crop water use, are determined by a biometeorlogical time scale model (Robertson, 1968) for cool season crops (wheat, barley etc.) and a Crop Heat Unit (Brown and Bootsma, 1993) algorithm for warm season crops (corn and soybean etc.).

This dataset is produced and published annually by Natural Resources Canada. It contains a variety of statistics on Canada’s mineral production, and provides the geographic locations of significant metallic, nonmetallic and coal mines, oil sands mines, selected metallurgical works and gas fields for the provinces and territories of Canada.Related product:- **[Top 100 Exploration Projects](https://open.canada.ca/data/en/dataset/b64179f3-ea0f-4abb-9cc5-85432fc958a0)**

Tow, catch, and length frequency for fish caught during the August sentinel surveys in the southern Gulf of St. Lawrence (NAFO Division 4T). Abundance indices and spatial distribution patterns of commercial groundfish.Note: Due to delays caused by logistic complexities and Covid the project did not take place in 2020

The Canadian indexes of social resilience and vulnerability were created to provide area-based information on resilience and vulnerability to natural hazards and disasters across Canada. Specifically, the Canadian Index of Social Resilience (CISR) aims to reflect a community’s ability to respond to and recover from natural hazards. In contrast, the Canadian Index of Social Vulnerability (CISV) aims to reflect the social vulnerability of an area based on factors that have the potential to amplify the impact of disasters on populations.Before the CISR and CISV were built, indicator frameworks were developed for social resilience and social vulnerability, respectively. Indicators were selected because of their demonstrated association with social resilience or social vulnerability. The selection was informed by the theoretical and research literature, existing indexes, availability of relevant data and engagement with subject-matter experts.The CISR and the CISV were created using data from Dissemination areas (DAs) across the country. The selected indicators were included in a principal component analysis, which is a statistical technique that allows a large number of indicators to be collapsed into a smaller number of interpretable components. Based on the results of the principal component analysis, DA-level scores were calculated for each index. Higher CISR scores correspond to DAs that are more resilient and higher CISV scores correspond to DAs that are more vulnerable.These indexes can be used to better understand areas which may experience the largest disproportional social impacts from natural hazards.

This dataset includes five depth-attenuated relative wave exposure index layers in raster format. Relative Exposure Index (REI) values are calculated based on effective fetch (derived from fetch values) combined with modelled wind data. The output REI layers are attenuated by depth, resulting in greater values in shallow, nearshore areas (Bekkby et al. 2008). The cell values represent an estimate of wave exposure at bottom depth normalized between regions from 0 (protected) to 1 (exposed).The objective of this dataset is to provide an estimate of wave exposure at bottom depth, primarily for use in species distribution modelling. Each single-band raster corresponds to a marine region, which generally coincide with the following layers from the Species Distribution Modelling Boundaries (https://www.gis-hub.ca/dataset/sdm-boundaries) dataset: Nearshore_HG, Nearshore_NCC, Nearshore_QCS, Nearshore_QCS, and Shelf_SalishSea. These layers extend to 50 m depth and up to 5 km from shore.Tabular data (csv files) are also included as part of the data package. These data are the calculated Relative Exposure Index (REI) values with fields for position information. The fetch values from gridded nearshore fetch (https://gis-hub.ca/dataset/gridded-nearshore-fetch) are used as a source dataset and the locations in the REI are the same as the gridded fetch.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6).CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset).Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios.Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale.Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

This is the spatial view used by Mineral Titles Online (MTO) on the mineral, placer and coal viewers. The spatial view combines the polygon information with attribute information for each title. Contains sub-surface title data in British Columbia for: - mineral claims, mining leases, mineral claim applications - placer claims, placer leases, placer claim applications - coal license applications, coal licenses, coal leases